Reinventing AWS cloud cost optimization using AI and FinOps.

In this blog, I share my experience in optimizing cloud spend using AWS and FinOps. Drawing on my CloudFlowAI project , I illustrate how continuous optimization can make cloud spend an asset.

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CloudFlowAI

Summary CloudFlowAI is a highly intelligent cloud-native application queueing hybrid system. It makes use of AI-driven decision-making to dynamically optimise message routing between on-premises and cloud environments in a cost-effective and performance-optimised fashion. Key Features AI-Powered Routing: Real-time message routing based on traffic conditions.

Cost Optimization: Forecasts and reduces cloud spending using predictive analytics.

Scalability: Auto-scales resources dynamically based on workload.

Compliance Monitoring: Tracks HIPAA & GDPR adherence.

Live Monitoring: Provides real-time metrics and insights to cloud engineers.

Setup git clone https://github.com/aadarshvision1/CloudFlowAI.git
cd CloudFlowAI
pip install -r requirements.txt
python main.py --config config.yaml
Configuration Modify config.yaml to tune:

AI learning rate

Cloud routing probability

Message latency thresholds

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